Comparative Analysis of Repeatability in CT Radiomics and Dosiomics Features under Image Perturbation: A Study in Cervical Cancer Patients.

This study aims to evaluate the repeatability of radiomics and dosiomics features via image perturbation of patients with cervical cancer. A total of 304 cervical cancer patients with planning CT images and dose maps were retrospectively included. Random translation, rotation, and contour randomization were applied to CT images and dose maps before radiomics feature extraction.

Comparing effectiveness of image perturbation and test retest imaging in improving radiomic model reliability.

Image perturbation is a promising technique to assess radiomic feature repeatability, but whether it can achieve the same effect as test-retest imaging on model reliability is unknown. This study aimed to compare radiomic model reliability based on repeatable features determined by the two methods using four different classifiers. A 191-patient public breast cancer dataset with 71 test-retest scans was used with pre-determined 117 training and 74 testing samples.

Study protocol of ConquerFear-HK: a randomised controlled trial of a metacognition-based, manualised intervention for fear of cancer recurrence among Chinese cancer survivors.

Introduction: Fear of cancer recurrence (FCR) is a prevalent and frequently debilitating response to a cancer diagnosis, affecting a substantial proportion of cancer survivors. Approximately 30% of local Hong Kong Chinese cancer survivors in a recent survey reportedly experienced persistent high FCR over the first-year post-surgery. This was associated with lower levels of psychological well-being and quality of life.

Improving radiomic model reliability using robust features from perturbations for head-and-neck carcinoma.

Background: Using high robust radiomic features in modeling is recommended, yet its impact on radiomic model is unclear. This study evaluated the radiomic model's robustness and generalizability after screening out low-robust features before radiomic modeling. The results were validated with four datasets and two clinically relevant tasks.

A Multi-Center Study of CT-Based Neck Nodal Radiomics for Predicting an Adaptive Radiotherapy Trigger of Ill-Fitted Thermoplastic Masks in Patients with Nasopharyngeal Carcinoma.

Significant lymph node shrinkage is common in patients with nasopharyngeal carcinoma (NPC) throughout radiotherapy (RT) treatment, causing ill-fitted thermoplastic masks (IfTMs). To deal with this, an ad hoc adaptive radiotherapy (ART) may be required to ensure accurate and safe radiation delivery and to maintain treatment efficacy. Presently, the entire procedure for evaluating an eligible ART candidate is time-consuming, resource-demanding, and highly inefficient.

Real-World Practice of Hypofractionated Radiotherapy in Patients With Invasive Breast Cancer.

Purpose: Application of hypofractionated radiotherapy (HFRT) is growing in patients with breast cancer (BC). This study aimed to explore a real-world practice of HFRT in early and locally advanced BC.

Methods: Patients with invasive BC between 2015 and 2019 were retrospectively reviewed.

Multi-Organ Omics-Based Prediction for Adaptive Radiation Therapy Eligibility in Nasopharyngeal Carcinoma Patients Undergoing Concurrent Chemoradiotherapy.

Purpose: To investigate the role of different multi-organ omics-based prediction models for pre-treatment prediction of Adaptive Radiotherapy (ART) eligibility in patients with nasopharyngeal carcinoma (NPC).

Methods And Materials: Pre-treatment contrast-enhanced computed tomographic and magnetic resonance images, radiotherapy dose and contour data of 135 NPC patients treated at Hong Kong Queen Elizabeth Hospital were retrospectively analyzed for extraction of multi-omics features, namely Radiomics (R), Morphology (M), Dosiomics (D), and Contouromics (C), from a total of eight organ structures. During model development, patient cohort was divided into a training set and a hold-out test set in a ratio of 7 to 3 20 iterations.

Virtual Contrast-Enhanced Magnetic Resonance Images Synthesis for Patients With Nasopharyngeal Carcinoma Using Multimodality-Guided Synergistic Neural Network.

Purpose: To investigate a novel deep-learning network that synthesizes virtual contrast-enhanced T1-weighted (vceT1w) magnetic resonance images (MRI) from multimodality contrast-free MRI for patients with nasopharyngeal carcinoma (NPC).

Methods And Materials: This article presents a retrospective analysis of multiparametric MRI, with and without contrast enhancement by gadolinium-based contrast agents (GBCAs), obtained from 64 biopsy-proven cases of NPC treated at Hong Kong Queen Elizabeth Hospital. A multimodality-guided synergistic neural network (MMgSN-Net) was developed to leverage complementary information between contrast-free T1-weighted and T2-weighted MRI for vceT1w MRI synthesis.

A cross-sectional study on the attitudes and perceptions of outpatients towards palliative care at the Hong Kong Queen Mary Hospital Hospice Centre.

Background: Palliative care aims to improve the quality of life for patients and their families, by helping them to cope with problems associated with illness. It targets four aspects of health: physical, psychological, social, and spiritual. Most of the current literature on palliative care is limited to the perspectives of health professionals.

Challenges for Quality Assurance of Target Volume Delineation in Clinical Trials.

In recent years, new radiotherapy techniques have emerged that aim to improve treatment outcome and reduce toxicity. The standard method of evaluating such techniques is to conduct large scale multicenter clinical trials, often across continents. A major challenge for such trials is quality assurance to ensure consistency of treatment across all participating centers.